Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making

Preparedness and Response Strategies for Chemical, Biological, Radiological, and Nuclear Incidents in the Middle East and North Africa: An Artificial Intelligence-Enhanced Delphi Approach

OBJECTIVE

Chemical, biological, radiological, and nuclear (CBRN) incidents require meticulous preparedness, particularly in the Middle East and North Africa (MENA) region. This study evaluated CBRN response operational flowcharts, tabletop training scenarios methods, and a health sector preparedness assessment tool specific to the MENA region.

METHODS

An online Delphi survey engaging international disaster medicine experts was conducted. Content validity indices (CVIs) were used to validate the items. Consensus metrics, including interquartile ranges (IQRs) and Kendall's W coefficient, were utilized to assess the panelists' agreement levels. Advanced artificial intelligence computing methods, including sentiment analysis and machine-learning methods (t-distributed stochastic neighbor embedding [t-SNE] and k-means), were used to cluster the consensus data.

RESULTS

Forty experts participated in this study. The item-level CVIs for the CBRN response flowcharts, preparedness assessment tool, and tabletop scenarios were 0.96, 0.85, and 0.84, respectively, indicating strong content validity. Consensus analysis demonstrated an IQR of 0 for most items and a strong Kendall's W coefficient, indicating a high level of agreement among the panelists. The t-SNE and k-means identified four clusters with greater European response engagement.

CONCLUSIONS

This study validated essential CBRN preparedness and response tools using broad expert consensus, demonstrating their applicability across different geographic areas.

Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography

Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups.

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry

IMTRODUCTION

The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) approaches, certain phenotypes may be confined to classify the affected population and to predict outcome. This study aimed to phenotype patients using unsupervised ML technique within the International Postgraduate Course Heart Failure Registry for patients hospitalized with COVID-19 and Cardiovascular disease and/or RF (PCHF-COVICAV).

MATERIAL AND METHODS

Patients from the eight centres with follow-up data available from the PCHF-COVICAV registry were included in this ML analysis (K-medoids algorithm).

RESULTS

Out of 617 patients included into the prospective part of the registry, 458 [median age: 76 (IQR:65-84) years, 55% male] were analyzed and 46 baseline variables, including demographics, clinical status, comorbidities and biochemical characteristics were incorporated into the ML. Three clusters were extracted by this ML method. Cluster 1 (n = 181) represents mainly women with the least number of overall comorbidities and cardiovascular RF. Cluster 2 (n = 227) is characterized mainly by men with non-CV conditions and less severe symptoms of infection. Cluster 3 (n=50) mainly represents men with the highest prevalence of cardiac comorbidities and RF, more extensive inflammation and organ dysfunction with the highest 6-month all-cause mortality risk.

CONCLUSIONS

The ML process has identified three important clinical clusters from hospitalized COVID-19 CV and/or RF patients. The cluster of males with severe CV disease, particularly HF, and multiple RF presenting with increased inflammation had a particularly poor outcome.

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

Design and implementation of a New Blockchain-based digital health passport: A Moroccan case study

In the context of COVID-19 pandemic, the Moroccan Interior and Health Ministries have proposed to use the health pass with a QR code to identify vaccinated people. Additionally, the government suggested a mobile application to control the health passport authenticity. However, the key problem is the possibility of anyone scanning the QR code and figuring out citizens' private information, causing severe issues about individual privacy. In this work, the main contribution is integrating a private Blockchain-based digital health passport to ensure high protection of sensitive information, security and privacy among all the actors (Government, Ministry of Interior, Ministry of Health, verifiers) that comply with the CNDP (National Commission for the Control of Personal Data Protection) and the Moroccan Law 09–08. In our proposed architectural framework solution, we identify two types of actors: authorized and unauthorized, to limit and control access to the citizens' personal information. Besides, to preserve individuals' privacy, we adopt on-chain and off-chain storage (Interplanetary File Systems IPFS). In our case, smart contracts improve security and privacy in the health passport verification process. Our system implementation describes the proposed solution to grant individual privacy. To verify and validate our approach, we used Remix-IDE and Ethereum Blockchain to build smart contracts.

Post-acute sequelae of COVID-19 symptom phenotypes and therapeutic strategies: A prospective, observational study

BACKGROUND

Post-acute sequelae of COVID-19 (PASC) includes a heterogeneous group of patients with variable symptomatology, who may respond to different therapeutic interventions. Identifying phenotypes of PASC and therapeutic strategies for different subgroups would be a major step forward in management.

METHODS

In a prospective cohort study of patients hospitalized with COVID-19, 12-month symptoms and quantitative outcome metrics were collected. Unsupervised hierarchical cluster analyses were performed to identify patients with: (1) similar symptoms lasting ≥4 weeks after acute SARS-CoV-2 infection, and (2) similar therapeutic interventions. Logistic regression analyses were used to evaluate the association of these symptom and therapy clusters with quantitative 12-month outcome metrics (modified Rankin Scale, Barthel Index, NIH NeuroQoL).

RESULTS

Among 242 patients, 122 (50%) reported ≥1 PASC symptom (median 3, IQR 1-5) lasting a median of 12-months (range 1-15) post-COVID diagnosis. Cluster analysis generated three symptom groups: Cluster1 had few symptoms (most commonly headache); Cluster2 had many symptoms including high levels of anxiety and depression; and Cluster3 primarily included shortness of breath, headache and cognitive symptoms. Cluster1 received few therapeutic interventions (OR 2.6, 95% CI 1.1-5.9), Cluster2 received several interventions, including antidepressants, anti-anxiety medications and psychological therapy (OR 15.7, 95% CI 4.1-59.7) and Cluster3 primarily received physical and occupational therapy (OR 3.1, 95%CI 1.3-7.1). The most severely affected patients (Symptom Cluster 2) had higher rates of disability (worse modified Rankin scores), worse NeuroQoL measures of anxiety, depression, fatigue and sleep disorder, and a higher number of stressors (all P<0.05). 100% of those who received a treatment strategy that included psychiatric therapies reported symptom improvement, compared to 97% who received primarily physical/occupational therapy, and 83% who received few interventions (P = 0.042).

CONCLUSIONS

We identified three clinically relevant PASC symptom-based phenotypes, which received different therapeutic interventions with varying response rates. These data may be helpful in tailoring individual treatment programs.